Apply Local Clustering Method to Improve the Running Speed of Ant Colony Optimization

摘要

Ant Colony Optimization (ACO) has time complexity O(t*m*N*N), and its typicalapplication is to solve Traveling Salesman Problem (TSP), where t, m, and Ndenotes the iteration number, number of ants, number of cities respectively.Cutting down running time is one of study focuses, and one way is to decreaseparameter t and N, especially N. For this focus, the following method ispresented in this paper. Firstly, design a novel clustering algorithm namedSpecial Local Clustering algorithm (SLC), then apply it to classify all citiesinto compact classes, where compact class is the class that all cities in thisclass cluster tightly in a small region. Secondly, let ACO act on every classto get a local TSP route. Thirdly, all local TSP routes are jointed to formsolution. Fourthly, the inaccuracy of solution caused by clustering iseliminated. Simulation shows that the presented method improves the runningspeed of ACO by 200 factors at least. And this high speed is benefit from twofactors. One is that class has small size and parameter N is cut down. Theroute length at every iteration step is convergent when ACO acts on compactclass. The other factor is that, using the convergence of route length astermination criterion of ACO and parameter t is cut down.